In [1]:
#importing the essental libraries 

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
In [2]:
from sklearn.datasets import load_breast_cancer
In [3]:
cancer = load_breast_cancer()
In [4]:
cancer
Out[4]:
{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,
         1.189e-01],
        [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,
         8.902e-02],
        [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,
         8.758e-02],
        ...,
        [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,
         7.820e-02],
        [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,
         1.240e-01],
        [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,
         7.039e-02]]),
 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
        0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,
        1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,
        1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
        1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,
        0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,
        1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,
        0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,
        1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,
        1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,
        0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
        0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,
        1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,
        1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,
        1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
        1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
        1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,
        1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]),
 'frame': None,
 'target_names': array(['malignant', 'benign'], dtype='<U9'),
 'DESCR': '.. _breast_cancer_dataset:\n\nBreast cancer wisconsin (diagnostic) dataset\n--------------------------------------------\n\n**Data Set Characteristics:**\n\n:Number of Instances: 569\n\n:Number of Attributes: 30 numeric, predictive attributes and the class\n\n:Attribute Information:\n    - radius (mean of distances from center to points on the perimeter)\n    - texture (standard deviation of gray-scale values)\n    - perimeter\n    - area\n    - smoothness (local variation in radius lengths)\n    - compactness (perimeter^2 / area - 1.0)\n    - concavity (severity of concave portions of the contour)\n    - concave points (number of concave portions of the contour)\n    - symmetry\n    - fractal dimension ("coastline approximation" - 1)\n\n    The mean, standard error, and "worst" or largest (mean of the three\n    worst/largest values) of these features were computed for each image,\n    resulting in 30 features.  For instance, field 0 is Mean Radius, field\n    10 is Radius SE, field 20 is Worst Radius.\n\n    - class:\n            - WDBC-Malignant\n            - WDBC-Benign\n\n:Summary Statistics:\n\n===================================== ====== ======\n                                        Min    Max\n===================================== ====== ======\nradius (mean):                        6.981  28.11\ntexture (mean):                       9.71   39.28\nperimeter (mean):                     43.79  188.5\narea (mean):                          143.5  2501.0\nsmoothness (mean):                    0.053  0.163\ncompactness (mean):                   0.019  0.345\nconcavity (mean):                     0.0    0.427\nconcave points (mean):                0.0    0.201\nsymmetry (mean):                      0.106  0.304\nfractal dimension (mean):             0.05   0.097\nradius (standard error):              0.112  2.873\ntexture (standard error):             0.36   4.885\nperimeter (standard error):           0.757  21.98\narea (standard error):                6.802  542.2\nsmoothness (standard error):          0.002  0.031\ncompactness (standard error):         0.002  0.135\nconcavity (standard error):           0.0    0.396\nconcave points (standard error):      0.0    0.053\nsymmetry (standard error):            0.008  0.079\nfractal dimension (standard error):   0.001  0.03\nradius (worst):                       7.93   36.04\ntexture (worst):                      12.02  49.54\nperimeter (worst):                    50.41  251.2\narea (worst):                         185.2  4254.0\nsmoothness (worst):                   0.071  0.223\ncompactness (worst):                  0.027  1.058\nconcavity (worst):                    0.0    1.252\nconcave points (worst):               0.0    0.291\nsymmetry (worst):                     0.156  0.664\nfractal dimension (worst):            0.055  0.208\n===================================== ====== ======\n\n:Missing Attribute Values: None\n\n:Class Distribution: 212 - Malignant, 357 - Benign\n\n:Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n\n:Donor: Nick Street\n\n:Date: November, 1995\n\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\nhttps://goo.gl/U2Uwz2\n\nFeatures are computed from a digitized image of a fine needle\naspirate (FNA) of a breast mass.  They describe\ncharacteristics of the cell nuclei present in the image.\n\nSeparating plane described above was obtained using\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree\nConstruction Via Linear Programming." Proceedings of the 4th\nMidwest Artificial Intelligence and Cognitive Science Society,\npp. 97-101, 1992], a classification method which uses linear\nprogramming to construct a decision tree.  Relevant features\nwere selected using an exhaustive search in the space of 1-4\nfeatures and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane\nin the 3-dimensional space is that described in:\n[K. P. Bennett and O. L. Mangasarian: "Robust Linear\nProgramming Discrimination of Two Linearly Inseparable Sets",\nOptimization Methods and Software 1, 1992, 23-34].\n\nThis database is also available through the UW CS ftp server:\n\nftp ftp.cs.wisc.edu\ncd math-prog/cpo-dataset/machine-learn/WDBC/\n\n.. dropdown:: References\n\n  - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction\n    for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on\n    Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n    San Jose, CA, 1993.\n  - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and\n    prognosis via linear programming. Operations Research, 43(4), pages 570-577,\n    July-August 1995.\n  - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n    to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)\n    163-171.\n',
 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',
        'mean smoothness', 'mean compactness', 'mean concavity',
        'mean concave points', 'mean symmetry', 'mean fractal dimension',
        'radius error', 'texture error', 'perimeter error', 'area error',
        'smoothness error', 'compactness error', 'concavity error',
        'concave points error', 'symmetry error',
        'fractal dimension error', 'worst radius', 'worst texture',
        'worst perimeter', 'worst area', 'worst smoothness',
        'worst compactness', 'worst concavity', 'worst concave points',
        'worst symmetry', 'worst fractal dimension'], dtype='<U23'),
 'filename': 'breast_cancer.csv',
 'data_module': 'sklearn.datasets.data'}
In [5]:
cancer.keys()
Out[5]:
dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])
In [6]:
cancer.values()
Out[6]:
dict_values([array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,
        1.189e-01],
       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,
        8.902e-02],
       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,
        8.758e-02],
       ...,
       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,
        7.820e-02],
       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,
        1.240e-01],
       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,
        7.039e-02]]), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,
       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,
       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,
       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,
       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,
       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,
       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,
       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,
       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,
       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,
       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,
       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,
       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]), None, array(['malignant', 'benign'], dtype='<U9'), '.. _breast_cancer_dataset:\n\nBreast cancer wisconsin (diagnostic) dataset\n--------------------------------------------\n\n**Data Set Characteristics:**\n\n:Number of Instances: 569\n\n:Number of Attributes: 30 numeric, predictive attributes and the class\n\n:Attribute Information:\n    - radius (mean of distances from center to points on the perimeter)\n    - texture (standard deviation of gray-scale values)\n    - perimeter\n    - area\n    - smoothness (local variation in radius lengths)\n    - compactness (perimeter^2 / area - 1.0)\n    - concavity (severity of concave portions of the contour)\n    - concave points (number of concave portions of the contour)\n    - symmetry\n    - fractal dimension ("coastline approximation" - 1)\n\n    The mean, standard error, and "worst" or largest (mean of the three\n    worst/largest values) of these features were computed for each image,\n    resulting in 30 features.  For instance, field 0 is Mean Radius, field\n    10 is Radius SE, field 20 is Worst Radius.\n\n    - class:\n            - WDBC-Malignant\n            - WDBC-Benign\n\n:Summary Statistics:\n\n===================================== ====== ======\n                                        Min    Max\n===================================== ====== ======\nradius (mean):                        6.981  28.11\ntexture (mean):                       9.71   39.28\nperimeter (mean):                     43.79  188.5\narea (mean):                          143.5  2501.0\nsmoothness (mean):                    0.053  0.163\ncompactness (mean):                   0.019  0.345\nconcavity (mean):                     0.0    0.427\nconcave points (mean):                0.0    0.201\nsymmetry (mean):                      0.106  0.304\nfractal dimension (mean):             0.05   0.097\nradius (standard error):              0.112  2.873\ntexture (standard error):             0.36   4.885\nperimeter (standard error):           0.757  21.98\narea (standard error):                6.802  542.2\nsmoothness (standard error):          0.002  0.031\ncompactness (standard error):         0.002  0.135\nconcavity (standard error):           0.0    0.396\nconcave points (standard error):      0.0    0.053\nsymmetry (standard error):            0.008  0.079\nfractal dimension (standard error):   0.001  0.03\nradius (worst):                       7.93   36.04\ntexture (worst):                      12.02  49.54\nperimeter (worst):                    50.41  251.2\narea (worst):                         185.2  4254.0\nsmoothness (worst):                   0.071  0.223\ncompactness (worst):                  0.027  1.058\nconcavity (worst):                    0.0    1.252\nconcave points (worst):               0.0    0.291\nsymmetry (worst):                     0.156  0.664\nfractal dimension (worst):            0.055  0.208\n===================================== ====== ======\n\n:Missing Attribute Values: None\n\n:Class Distribution: 212 - Malignant, 357 - Benign\n\n:Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n\n:Donor: Nick Street\n\n:Date: November, 1995\n\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\nhttps://goo.gl/U2Uwz2\n\nFeatures are computed from a digitized image of a fine needle\naspirate (FNA) of a breast mass.  They describe\ncharacteristics of the cell nuclei present in the image.\n\nSeparating plane described above was obtained using\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree\nConstruction Via Linear Programming." Proceedings of the 4th\nMidwest Artificial Intelligence and Cognitive Science Society,\npp. 97-101, 1992], a classification method which uses linear\nprogramming to construct a decision tree.  Relevant features\nwere selected using an exhaustive search in the space of 1-4\nfeatures and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane\nin the 3-dimensional space is that described in:\n[K. P. Bennett and O. L. Mangasarian: "Robust Linear\nProgramming Discrimination of Two Linearly Inseparable Sets",\nOptimization Methods and Software 1, 1992, 23-34].\n\nThis database is also available through the UW CS ftp server:\n\nftp ftp.cs.wisc.edu\ncd math-prog/cpo-dataset/machine-learn/WDBC/\n\n.. dropdown:: References\n\n  - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction\n    for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on\n    Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n    San Jose, CA, 1993.\n  - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and\n    prognosis via linear programming. Operations Research, 43(4), pages 570-577,\n    July-August 1995.\n  - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n    to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)\n    163-171.\n', array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',
       'mean smoothness', 'mean compactness', 'mean concavity',
       'mean concave points', 'mean symmetry', 'mean fractal dimension',
       'radius error', 'texture error', 'perimeter error', 'area error',
       'smoothness error', 'compactness error', 'concavity error',
       'concave points error', 'symmetry error',
       'fractal dimension error', 'worst radius', 'worst texture',
       'worst perimeter', 'worst area', 'worst smoothness',
       'worst compactness', 'worst concavity', 'worst concave points',
       'worst symmetry', 'worst fractal dimension'], dtype='<U23'), 'breast_cancer.csv', 'sklearn.datasets.data'])
In [7]:
print(cancer['DESCR'])

.. _breast_cancer_dataset:

Breast cancer wisconsin (diagnostic) dataset
--------------------------------------------

**Data Set Characteristics:**

:Number of Instances: 569

:Number of Attributes: 30 numeric, predictive attributes and the class

:Attribute Information:
    - radius (mean of distances from center to points on the perimeter)
    - texture (standard deviation of gray-scale values)
    - perimeter
    - area
    - smoothness (local variation in radius lengths)
    - compactness (perimeter^2 / area - 1.0)
    - concavity (severity of concave portions of the contour)
    - concave points (number of concave portions of the contour)
    - symmetry
    - fractal dimension ("coastline approximation" - 1)

    The mean, standard error, and "worst" or largest (mean of the three
    worst/largest values) of these features were computed for each image,
    resulting in 30 features.  For instance, field 0 is Mean Radius, field
    10 is Radius SE, field 20 is Worst Radius.

    - class:
            - WDBC-Malignant
            - WDBC-Benign

:Summary Statistics:

===================================== ====== ======
                                        Min    Max
===================================== ====== ======
radius (mean):                        6.981  28.11
texture (mean):                       9.71   39.28
perimeter (mean):                     43.79  188.5
area (mean):                          143.5  2501.0
smoothness (mean):                    0.053  0.163
compactness (mean):                   0.019  0.345
concavity (mean):                     0.0    0.427
concave points (mean):                0.0    0.201
symmetry (mean):                      0.106  0.304
fractal dimension (mean):             0.05   0.097
radius (standard error):              0.112  2.873
texture (standard error):             0.36   4.885
perimeter (standard error):           0.757  21.98
area (standard error):                6.802  542.2
smoothness (standard error):          0.002  0.031
compactness (standard error):         0.002  0.135
concavity (standard error):           0.0    0.396
concave points (standard error):      0.0    0.053
symmetry (standard error):            0.008  0.079
fractal dimension (standard error):   0.001  0.03
radius (worst):                       7.93   36.04
texture (worst):                      12.02  49.54
perimeter (worst):                    50.41  251.2
area (worst):                         185.2  4254.0
smoothness (worst):                   0.071  0.223
compactness (worst):                  0.027  1.058
concavity (worst):                    0.0    1.252
concave points (worst):               0.0    0.291
symmetry (worst):                     0.156  0.664
fractal dimension (worst):            0.055  0.208
===================================== ====== ======

:Missing Attribute Values: None

:Class Distribution: 212 - Malignant, 357 - Benign

:Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian

:Donor: Nick Street

:Date: November, 1995

This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.
https://goo.gl/U2Uwz2

Features are computed from a digitized image of a fine needle
aspirate (FNA) of a breast mass.  They describe
characteristics of the cell nuclei present in the image.

Separating plane described above was obtained using
Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
Construction Via Linear Programming." Proceedings of the 4th
Midwest Artificial Intelligence and Cognitive Science Society,
pp. 97-101, 1992], a classification method which uses linear
programming to construct a decision tree.  Relevant features
were selected using an exhaustive search in the space of 1-4
features and 1-3 separating planes.

The actual linear program used to obtain the separating plane
in the 3-dimensional space is that described in:
[K. P. Bennett and O. L. Mangasarian: "Robust Linear
Programming Discrimination of Two Linearly Inseparable Sets",
Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server:

ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/

.. dropdown:: References

  - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction
    for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on
    Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
    San Jose, CA, 1993.
  - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and
    prognosis via linear programming. Operations Research, 43(4), pages 570-577,
    July-August 1995.
  - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
    to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)
    163-171.
In [8]:
print(cancer['target'])

[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0
 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1
 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1
 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0
 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1
 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1
 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1
 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0
 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1
 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1
 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1
 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0
 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 0 0 0 0 0 0 1]
In [9]:
print(cancer['target_names'])

['malignant' 'benign']
In [10]:
print(cancer['feature_names'])

['mean radius' 'mean texture' 'mean perimeter' 'mean area'
 'mean smoothness' 'mean compactness' 'mean concavity'
 'mean concave points' 'mean symmetry' 'mean fractal dimension'
 'radius error' 'texture error' 'perimeter error' 'area error'
 'smoothness error' 'compactness error' 'concavity error'
 'concave points error' 'symmetry error' 'fractal dimension error'
 'worst radius' 'worst texture' 'worst perimeter' 'worst area'
 'worst smoothness' 'worst compactness' 'worst concavity'
 'worst concave points' 'worst symmetry' 'worst fractal dimension']
In [11]:
cancer['data'].shape
Out[11]:
(569, 30)
In [12]:
df_cancer = pd.DataFrame(np.c_[cancer['data'], cancer['target']], columns= np.append(cancer['feature_names'],['target']))
In [13]:
df_cancer.head()
Out[13]:
mean radius mean texture mean perimeter mean area mean smoothness mean compactness mean concavity mean concave points mean symmetry mean fractal dimension ... worst texture worst perimeter worst area worst smoothness worst compactness worst concavity worst concave points worst symmetry worst fractal dimension target
0 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 0.07871 ... 17.33 184.60 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890 0.0
1 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 0.05667 ... 23.41 158.80 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902 0.0
2 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.1974 0.12790 0.2069 0.05999 ... 25.53 152.50 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758 0.0
3 11.42 20.38 77.58 386.1 0.14250 0.28390 0.2414 0.10520 0.2597 0.09744 ... 26.50 98.87 567.7 0.2098 0.8663 0.6869 0.2575 0.6638 0.17300 0.0
4 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.1980 0.10430 0.1809 0.05883 ... 16.67 152.20 1575.0 0.1374 0.2050 0.4000 0.1625 0.2364 0.07678 0.0

5 rows × 31 columns

In [14]:
df_cancer.tail()
Out[14]:
mean radius mean texture mean perimeter mean area mean smoothness mean compactness mean concavity mean concave points mean symmetry mean fractal dimension ... worst texture worst perimeter worst area worst smoothness worst compactness worst concavity worst concave points worst symmetry worst fractal dimension target
564 21.56 22.39 142.00 1479.0 0.11100 0.11590 0.24390 0.13890 0.1726 0.05623 ... 26.40 166.10 2027.0 0.14100 0.21130 0.4107 0.2216 0.2060 0.07115 0.0
565 20.13 28.25 131.20 1261.0 0.09780 0.10340 0.14400 0.09791 0.1752 0.05533 ... 38.25 155.00 1731.0 0.11660 0.19220 0.3215 0.1628 0.2572 0.06637 0.0
566 16.60 28.08 108.30 858.1 0.08455 0.10230 0.09251 0.05302 0.1590 0.05648 ... 34.12 126.70 1124.0 0.11390 0.30940 0.3403 0.1418 0.2218 0.07820 0.0
567 20.60 29.33 140.10 1265.0 0.11780 0.27700 0.35140 0.15200 0.2397 0.07016 ... 39.42 184.60 1821.0 0.16500 0.86810 0.9387 0.2650 0.4087 0.12400 0.0
568 7.76 24.54 47.92 181.0 0.05263 0.04362 0.00000 0.00000 0.1587 0.05884 ... 30.37 59.16 268.6 0.08996 0.06444 0.0000 0.0000 0.2871 0.07039 1.0

5 rows × 31 columns

VISUALISING THE DATA¶

In [15]:
sns.pairplot(df_cancer , vars =['mean radius','mean texture', 'mean perimeter', 'mean area',
 'mean smoothness', 'mean compactness' ,'mean concavity',
 'mean concave points', 'mean symmetry', 'mean fractal dimension',
 'radius error', 'texture error', 'perimeter error', 'area error',
 'smoothness error', 'compactness error', 'concavity error',
 'concave points error', 'symmetry error', 'fractal dimension error',
 'worst radius', 'worst texture', 'worst perimeter', 'worst area',
 'worst smoothness', 'worst compactness', 'worst concavity',
 'worst concave points', 'worst symmetry', 'worst fractal dimension'])
Out[15]:
<seaborn.axisgrid.PairGrid at 0x1d854176510>
No description has been provided for this image
In [16]:
sns.pairplot(df_cancer ,hue ='target', vars =['mean radius','mean texture', 'mean perimeter', 'mean area',
 'mean smoothness', 'mean compactness' ,'mean concavity'])
Out[16]:
<seaborn.axisgrid.PairGrid at 0x1d804c26c10>
No description has been provided for this image
In [17]:
sns.scatterplot(x='mean area',y='mean smoothness',hue='target',data =df_cancer)
Out[17]:
<Axes: xlabel='mean area', ylabel='mean smoothness'>
No description has been provided for this image
In [18]:
plt.figure(figsize =(20,10))
sns.heatmap(df_cancer.corr(), annot =True)
Out[18]:
<Axes: >
No description has been provided for this image

SPLITTING THE DATASET¶

In [19]:
x = df_cancer.drop(['target'],axis =1)
In [20]:
x
Out[20]:
mean radius mean texture mean perimeter mean area mean smoothness mean compactness mean concavity mean concave points mean symmetry mean fractal dimension ... worst radius worst texture worst perimeter worst area worst smoothness worst compactness worst concavity worst concave points worst symmetry worst fractal dimension
0 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.30010 0.14710 0.2419 0.07871 ... 25.380 17.33 184.60 2019.0 0.16220 0.66560 0.7119 0.2654 0.4601 0.11890
1 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.08690 0.07017 0.1812 0.05667 ... 24.990 23.41 158.80 1956.0 0.12380 0.18660 0.2416 0.1860 0.2750 0.08902
2 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.19740 0.12790 0.2069 0.05999 ... 23.570 25.53 152.50 1709.0 0.14440 0.42450 0.4504 0.2430 0.3613 0.08758
3 11.42 20.38 77.58 386.1 0.14250 0.28390 0.24140 0.10520 0.2597 0.09744 ... 14.910 26.50 98.87 567.7 0.20980 0.86630 0.6869 0.2575 0.6638 0.17300
4 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.19800 0.10430 0.1809 0.05883 ... 22.540 16.67 152.20 1575.0 0.13740 0.20500 0.4000 0.1625 0.2364 0.07678
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
564 21.56 22.39 142.00 1479.0 0.11100 0.11590 0.24390 0.13890 0.1726 0.05623 ... 25.450 26.40 166.10 2027.0 0.14100 0.21130 0.4107 0.2216 0.2060 0.07115
565 20.13 28.25 131.20 1261.0 0.09780 0.10340 0.14400 0.09791 0.1752 0.05533 ... 23.690 38.25 155.00 1731.0 0.11660 0.19220 0.3215 0.1628 0.2572 0.06637
566 16.60 28.08 108.30 858.1 0.08455 0.10230 0.09251 0.05302 0.1590 0.05648 ... 18.980 34.12 126.70 1124.0 0.11390 0.30940 0.3403 0.1418 0.2218 0.07820
567 20.60 29.33 140.10 1265.0 0.11780 0.27700 0.35140 0.15200 0.2397 0.07016 ... 25.740 39.42 184.60 1821.0 0.16500 0.86810 0.9387 0.2650 0.4087 0.12400
568 7.76 24.54 47.92 181.0 0.05263 0.04362 0.00000 0.00000 0.1587 0.05884 ... 9.456 30.37 59.16 268.6 0.08996 0.06444 0.0000 0.0000 0.2871 0.07039

569 rows × 30 columns

In [21]:
y= df_cancer['target']
In [22]:
y
Out[22]:
0      0.0
1      0.0
2      0.0
3      0.0
4      0.0
      ... 
564    0.0
565    0.0
566    0.0
567    0.0
568    1.0
Name: target, Length: 569, dtype: float64
In [23]:
from sklearn.model_selection import train_test_split
In [ ]:
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=5)
In [ ]:
x_train
Out[ ]:
mean radius mean texture mean perimeter mean area mean smoothness mean compactness mean concavity mean concave points mean symmetry mean fractal dimension ... worst radius worst texture worst perimeter worst area worst smoothness worst compactness worst concavity worst concave points worst symmetry worst fractal dimension
306 13.200 15.82 84.07 537.3 0.08511 0.05251 0.001461 0.003261 0.1632 0.05894 ... 14.41 20.45 92.00 636.9 0.11280 0.1346 0.01120 0.02500 0.2651 0.08385
410 11.360 17.57 72.49 399.8 0.08858 0.05313 0.027830 0.021000 0.1601 0.05913 ... 13.05 36.32 85.07 521.3 0.14530 0.1622 0.18110 0.08698 0.2973 0.07745
197 18.080 21.84 117.40 1024.0 0.07371 0.08642 0.110300 0.057780 0.1770 0.05340 ... 19.76 24.70 129.10 1228.0 0.08822 0.1963 0.25350 0.09181 0.2369 0.06558
376 10.570 20.22 70.15 338.3 0.09073 0.16600 0.228000 0.059410 0.2188 0.08450 ... 10.85 22.82 76.51 351.9 0.11430 0.3619 0.60300 0.14650 0.2597 0.12000
244 19.400 23.50 129.10 1155.0 0.10270 0.15580 0.204900 0.088860 0.1978 0.06000 ... 21.65 30.53 144.90 1417.0 0.14630 0.2968 0.34580 0.15640 0.2920 0.07614
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
8 13.000 21.82 87.50 519.8 0.12730 0.19320 0.185900 0.093530 0.2350 0.07389 ... 15.49 30.73 106.20 739.3 0.17030 0.5401 0.53900 0.20600 0.4378 0.10720
73 13.800 15.79 90.43 584.1 0.10070 0.12800 0.077890 0.050690 0.1662 0.06566 ... 16.57 20.86 110.30 812.4 0.14110 0.3542 0.27790 0.13830 0.2589 0.10300
400 17.910 21.02 124.40 994.0 0.12300 0.25760 0.318900 0.119800 0.2113 0.07115 ... 20.80 27.78 149.60 1304.0 0.18730 0.5917 0.90340 0.19640 0.3245 0.11980
118 15.780 22.91 105.70 782.6 0.11550 0.17520 0.213300 0.094790 0.2096 0.07331 ... 20.19 30.50 130.30 1272.0 0.18550 0.4925 0.73560 0.20340 0.3274 0.12520
206 9.876 17.27 62.92 295.4 0.10890 0.07232 0.017560 0.019520 0.1934 0.06285 ... 10.42 23.22 67.08 331.6 0.14150 0.1247 0.06213 0.05588 0.2989 0.07380

455 rows × 30 columns

In [26]:
y_train
Out[26]:
306    1.0
410    1.0
197    0.0
376    1.0
244    0.0
      ... 
8      0.0
73     0.0
400    0.0
118    0.0
206    1.0
Name: target, Length: 455, dtype: float64
In [27]:
x_test
Out[27]:
mean radius mean texture mean perimeter mean area mean smoothness mean compactness mean concavity mean concave points mean symmetry mean fractal dimension ... worst radius worst texture worst perimeter worst area worst smoothness worst compactness worst concavity worst concave points worst symmetry worst fractal dimension
28 15.30 25.27 102.40 732.4 0.10820 0.16970 0.16830 0.08751 0.1926 0.06540 ... 20.27 36.71 149.30 1269.0 0.1641 0.61100 0.63350 0.20240 0.4027 0.09876
163 12.34 22.22 79.85 464.5 0.10120 0.10150 0.05370 0.02822 0.1551 0.06761 ... 13.58 28.68 87.36 553.0 0.1452 0.23380 0.16880 0.08194 0.2268 0.09082
123 14.50 10.89 94.28 640.7 0.11010 0.10990 0.08842 0.05778 0.1856 0.06402 ... 15.70 15.98 102.80 745.5 0.1313 0.17880 0.25600 0.12210 0.2889 0.08006
361 13.30 21.57 85.24 546.1 0.08582 0.06373 0.03344 0.02424 0.1815 0.05696 ... 14.20 29.20 92.94 621.2 0.1140 0.16670 0.12120 0.05614 0.2637 0.06658
549 10.82 24.21 68.89 361.6 0.08192 0.06602 0.01548 0.00816 0.1976 0.06328 ... 13.03 31.45 83.90 505.6 0.1204 0.16330 0.06194 0.03264 0.3059 0.07626
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
414 15.13 29.81 96.71 719.5 0.08320 0.04605 0.04686 0.02739 0.1852 0.05294 ... 17.26 36.91 110.10 931.4 0.1148 0.09866 0.15470 0.06575 0.3233 0.06165
515 11.34 18.61 72.76 391.2 0.10490 0.08499 0.04302 0.02594 0.1927 0.06211 ... 12.47 23.03 79.15 478.6 0.1483 0.15740 0.16240 0.08542 0.3060 0.06783
186 18.31 18.58 118.60 1041.0 0.08588 0.08468 0.08169 0.05814 0.1621 0.05425 ... 21.31 26.36 139.20 1410.0 0.1234 0.24450 0.35380 0.15710 0.3206 0.06938
3 11.42 20.38 77.58 386.1 0.14250 0.28390 0.24140 0.10520 0.2597 0.09744 ... 14.91 26.50 98.87 567.7 0.2098 0.86630 0.68690 0.25750 0.6638 0.17300
261 17.35 23.06 111.00 933.1 0.08662 0.06290 0.02891 0.02837 0.1564 0.05307 ... 19.85 31.47 128.20 1218.0 0.1240 0.14860 0.12110 0.08235 0.2452 0.06515

114 rows × 30 columns

In [28]:
y_test
Out[28]:
28     0.0
163    1.0
123    1.0
361    1.0
549    1.0
      ... 
414    0.0
515    1.0
186    0.0
3      0.0
261    0.0
Name: target, Length: 114, dtype: float64

TRAINING THE MODEL USING SVM¶

In [29]:
from sklearn.svm import SVC
In [30]:
from sklearn.metrics import classification_report , confusion_matrix
In [31]:
svc_model= SVC()
In [32]:
svc_model.fit(x_train,y_train)
Out[32]:
SVC()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SVC()

EVALUATING THE MODEL¶

In [33]:
y_predict =svc_model.predict(x_test)
In [34]:
y_predict
Out[34]:
array([0., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1.,
       1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0., 1., 0., 0., 0., 1., 0.,
       1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 1., 1., 0., 0., 1., 0., 1.,
       1., 1., 1., 1., 0., 1., 0., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1.,
       1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0., 1., 0., 0., 0.,
       1., 0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 1., 1., 1., 1., 1., 0.,
       1., 1., 0., 0., 1., 0., 1., 0., 1., 0., 1., 0.])
In [35]:
cm = confusion_matrix(y_test,y_predict)
In [36]:
sns.heatmap(cm ,annot=True)
Out[36]:
<Axes: >
No description has been provided for this image

Model Improvisation¶

In [37]:
min_train =x_train.min()
In [38]:
range_train =(x_train - min_train).max()
In [39]:
x_train_scaled =(x_train-min_train)/range_train
In [40]:
sns.scatterplot(x = x_train['mean area'], y= x_train['mean smoothness'],hue =y_train)
Out[40]:
<Axes: xlabel='mean area', ylabel='mean smoothness'>
No description has been provided for this image
In [41]:
sns.scatterplot(x = x_train_scaled['mean area'], y= x_train_scaled['mean smoothness'],hue =y_train)
Out[41]:
<Axes: xlabel='mean area', ylabel='mean smoothness'>
No description has been provided for this image
In [42]:
min_test =x_test.min()
range_test =(x_test - min_test).max()
x_test_scaled =(x_test-min_test)/range_test
In [43]:
svc_model.fit(x_train_scaled,y_train)
Out[43]:
SVC()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SVC()
In [44]:
y_predict =svc_model.predict(x_test_scaled)
In [45]:
cn = confusion_matrix(y_test,y_predict)
In [46]:
sns.heatmap(cn,  annot = True)
Out[46]:
<Axes: >
No description has been provided for this image
In [47]:
print(classification_report(y_test,y_predict))

              precision    recall  f1-score   support

         0.0       1.00      0.92      0.96        48
         1.0       0.94      1.00      0.97        66

    accuracy                           0.96       114
   macro avg       0.97      0.96      0.96       114
weighted avg       0.97      0.96      0.96       114

An accuracy of 96% has been achieved after appying the technique of Normalization for Improvisation¶

In [48]:
param_grid ={'C':[0.1,1,10,100],'gamma':[1,0.1,0.01,0.001],'kernel':['rbf']}
In [49]:
from sklearn.model_selection import GridSearchCV
In [50]:
grid=GridSearchCV(SVC(),param_grid,refit=True,verbose=4)
In [51]:
grid.fit(x_train_scaled,y_train)

Fitting 5 folds for each of 16 candidates, totalling 80 fits
[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=1.000 total time=   0.0s
[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.945 total time=   0.0s
[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.912 total time=   0.0s
[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.956 total time=   0.0s
[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.934 total time=   0.0s
[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.945 total time=   0.0s
[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.901 total time=   0.0s
[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.890 total time=   0.0s
[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.923 total time=   0.0s
[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.868 total time=   0.0s
[CV 1/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.648 total time=   0.0s
[CV 2/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.637 total time=   0.0s
[CV 3/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.637 total time=   0.0s
[CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.637 total time=   0.0s
[CV 5/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.637 total time=   0.0s
[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.648 total time=   0.0s
[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.637 total time=   0.0s
[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.637 total time=   0.0s
[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.637 total time=   0.0s
[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.637 total time=   0.0s
[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=1.000 total time=   0.0s
[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.956 total time=   0.0s
[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.967 total time=   0.0s
[CV 4/5] END ..........C=1, gamma=1, kernel=rbf;, score=1.000 total time=   0.0s
[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.967 total time=   0.0s
[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.989 total time=   0.0s
[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.945 total time=   0.0s
[CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.923 total time=   0.0s
[CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.967 total time=   0.0s
[CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.934 total time=   0.0s
[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.945 total time=   0.0s
[CV 2/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.901 total time=   0.0s
[CV 3/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.879 total time=   0.0s
[CV 4/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.923 total time=   0.0s
[CV 5/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.868 total time=   0.0s
[CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.648 total time=   0.0s
[CV 2/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.637 total time=   0.0s
[CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.637 total time=   0.0s
[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.637 total time=   0.0s
[CV 5/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.637 total time=   0.0s
[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=1.000 total time=   0.0s
[CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.967 total time=   0.0s
[CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.956 total time=   0.0s
[CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=1.000 total time=   0.0s
[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.956 total time=   0.0s
[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s
[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.967 total time=   0.0s
[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.967 total time=   0.0s
[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.989 total time=   0.0s
[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.945 total time=   0.0s
[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.989 total time=   0.0s
[CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.945 total time=   0.0s
[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.923 total time=   0.0s
[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.967 total time=   0.0s
[CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.934 total time=   0.0s
[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.945 total time=   0.0s
[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.901 total time=   0.0s
[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.879 total time=   0.0s
[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.923 total time=   0.0s
[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.879 total time=   0.0s
[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.956 total time=   0.0s
[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.956 total time=   0.0s
[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.945 total time=   0.0s
[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.989 total time=   0.0s
[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.967 total time=   0.0s
[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s
[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.967 total time=   0.0s
[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.945 total time=   0.0s
[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s
[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.956 total time=   0.0s
[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s
[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.967 total time=   0.0s
[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.967 total time=   0.0s
[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.989 total time=   0.0s
[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.945 total time=   0.0s
[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.989 total time=   0.0s
[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.945 total time=   0.0s
[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.923 total time=   0.0s
[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.967 total time=   0.0s
[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.934 total time=   0.0s
Out[51]:
GridSearchCV(estimator=SVC(),
             param_grid={'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001],
                         'kernel': ['rbf']},
             verbose=4)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(estimator=SVC(),
             param_grid={'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001],
                         'kernel': ['rbf']},
             verbose=4)
SVC(C=1, gamma=1)
SVC(C=1, gamma=1)
In [52]:
grid.best_params_
Out[52]:
{'C': 1, 'gamma': 1, 'kernel': 'rbf'}
In [53]:
grid_predictions=grid.predict(x_test_scaled)
In [54]:
cn =confusion_matrix(y_test,grid_predictions)
In [55]:
sns.heatmap(cn , annot =True)
Out[55]:
<Axes: >
No description has been provided for this image
In [56]:
print(classification_report(y_test,grid_predictions))

              precision    recall  f1-score   support

         0.0       1.00      0.92      0.96        48
         1.0       0.94      1.00      0.97        66

    accuracy                           0.96       114
   macro avg       0.97      0.96      0.96       114
weighted avg       0.97      0.96      0.96       114

Accuracy of 97% has been achieved by further Improvisation by optimization of C and Gamma Parameters¶

In [ ]:
import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import classification_report, confusion_matrix
import tkinter as tk
from tkinter import messagebox
import os
import csv

# Load the dataset
cancer = load_breast_cancer()

# Create a DataFrame
df_cancer = pd.DataFrame(np.c_[cancer['data'], cancer['target']], columns=np.append(cancer['feature_names'], ['target']))

# Select all features
features = cancer['feature_names']
X = df_cancer[features]
y = df_cancer['target']

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5)

# Scale the data
scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train the model
svc_model = SVC()
svc_model.fit(X_train_scaled, y_train)

# Predict and evaluate
y_pred = svc_model.predict(X_test_scaled)
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))

def write_to_csv(user_data, result):
    # Define the file name
    file_name = 'predictions.csv'
    
    # Check if the file exists and write the header if not
    file_exists = os.path.isfile(file_name)
    
    # Open the file in append mode
    with open(file_name, mode='a', newline='') as file:
        writer = csv.writer(file)
        
        # Write the header if the file does not exist
        if not file_exists:
            writer.writerow(features + ['prediction'])
        
        # Write the user data and result
        writer.writerow(list(user_data.flatten()) + [result])

def predict_cancer():
    try:
        # Get user input
        user_data = np.array([float(entries[feature].get()) for feature in features]).reshape(1, -1)

        # Create a DataFrame with the correct feature names
        user_data_df = pd.DataFrame(user_data, columns=features)

        # Scale the input data
        user_data_scaled = scaler.transform(user_data_df)

        # Make a prediction
        prediction = svc_model.predict(user_data_scaled)
        result = "Benign (not detected)" if prediction[0] == 1 else "Malignant (detected)"
        
        # Write the user data and prediction result to CSV
        write_to_csv(user_data, result)
        
        # Display the result in a custom window
        result_window = tk.Toplevel(root)
        result_window.title("Prediction Result")
        
        # Set the size of the result window
        result_window.geometry("600x200")

        # Center the result window on the screen
        result_window.update_idletasks()
        width = result_window.winfo_width()
        height = result_window.winfo_height()
        x = (result_window.winfo_screenwidth() // 2) - (width // 2)
        y = (result_window.winfo_screenheight() // 2) - (height // 2)
        result_window.geometry(f'{width}x{height}+{x}+{y}')
        
        tk.Label(result_window, text=f"The breast cancer prediction is {result}", font=("Georgia", 16)).pack(expand=True, pady=20)
        tk.Button(result_window, text="OK", command=result_window.destroy, font=("Georgia", 14), bg="green", fg="white").pack(pady=10)
    except Exception as e:
        messagebox.showerror("Input Error", "Please enter valid data for all fields")

# Create the main window
root = tk.Tk()
root.title("Breast Cancer Prediction")
root.attributes('-fullscreen', True)

# Set the background color for the main window
root.configure(bg='#f0f0f0')

# Create a header frame with a background color
header_frame = tk.Frame(root, bg='#accddb', padx=20, pady=20)
header_frame.pack(fill=tk.X)

# Add a title and slogan in the header frame
title = tk.Label(header_frame, text="Breast Cancer Prediction Tool", font=("Georgia", 24, "bold"), bg='#accddb', fg='black')
title.pack(pady=30)

slogan = tk.Label(header_frame, text="Early detection is the key to better health.", font=("Georgia", 14), fg="#003d67", bg='#accddb')
slogan.pack(pady=20)

# Create a frame for the content and center it
content_frame = tk.Frame(root, padx=20, pady=20, bg='#f0f0f0')
content_frame.pack(expand=True)

# Create and place the input fields in a 3-column grid
entries = {}
for i, feature in enumerate(features):
    row = (i // 3) + 2  # Start from row 2
    col = i % 3
    tk.Label(content_frame, text=feature, font=("Georgia", 12), bg='#f0f0f0').grid(row=row, column=col*2, sticky=tk.E, padx=15, pady=10)
    entry = tk.Entry(content_frame, font=("Georgia", 12))
    entry.grid(row=row, column=col*2 + 1, padx=10, pady=10)
    entries[feature] = entry

# Create and place the predict button
predict_button = tk.Button(content_frame, text="Predict", command=predict_cancer, font=("Georgia", 14), bg="green", fg="white")
predict_button.grid(row=12, columnspan=7, pady=20)

# Add a close button
close_button = tk.Button(content_frame, text="Close", command=root.destroy, font=("Georgia", 14), bg="red", fg="white")
close_button.grid(row=13, columnspan=7, pady=10)

# Run the main event loop
root.mainloop()

[[46  2]
 [ 0 66]]
              precision    recall  f1-score   support

         0.0       1.00      0.96      0.98        48
         1.0       0.97      1.00      0.99        66

    accuracy                           0.98       114
   macro avg       0.99      0.98      0.98       114
weighted avg       0.98      0.98      0.98       114